Neurological Worsening After Implantable Pulse Generator Replacement
Bibliographic record
Abstract
BACKGROUND: Most of the implantable pulse generators (IPGs) in deep brain stimulation (DBS) used to date are non-rechargeable requiring regular replacements. IPG replacement is a minor surgical procedure, but adverse events including neurological worsening have been reported. In this study, we determine the possibility of neurological worsening after IPG replacement in Parkinson's disease (PD) cases on chronic DBS therapy (CDT) and its electrophysiological basis along with the therapeutic interventions used to alleviate them. METHODS: This study is a retrospective chart review of PD cases on CDT followed at London Movement Disorders Centre from January 2010 to December 2016. Included cases were those who underwent one or more IPG replacement. RESULTS: A total of 45 PD cases on CDT underwent 62 IPG replacements involving 121 channels. Neurological worsening was observed in 16 (35.5%) cases following 17 (27.4%) IPG replacements, all following dual-channel IPG replacements. Tremor (41.2%), speech (35.3%), and gait (23.5%) worsened most commonly. Deviation from the pre-replacement parameters including voltage and impedance resulting in change in total electrical energy delivered (TEED) was the most common electrophysiological correlate, observed in 82.4% (14/17) IPGs having neurological worsening. This included switched laterality in a dual-channel IPG. Neurological worsening in the remaining 17.6% cases was hardware-related. CONCLUSION: Neurological worsening followed 27.4% of IPG replacements in PD cases on CDT with approximately 82.4% of these being avoidable by carefully monitoring stimulation parameters to match pre-replacement TEED values.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".